Top Of Atmosphere Reflectance Research Articles

A novel prediction framework for estimating high spatial resolution near-ground PM2.5 and O3 concentrations at street-level in urban areas

National monitoring stations lack spatial coverage for reflecting microscopic changes in air pollution. Several studies have attempted to use locally measured data to develop prediction models to complement national stations. However, the lack of meteorological stations in urban areas makes it challenging to obtain temperature (TEM) and relative humidity (RH) with high spatial resolution; since these are important air pollution predictors, these models cannot be applied to entire urban areas. Here, we propose a new prediction framework that estimates near-ground high spatial resolution PM2.5 and O3 concentrations based on short-time and large-scale monitoring and multisource urban data. We conducted a mobile monitoring experiment in Wuhan using electric bicycles to collect PM2.5 and O3 concentrations and TEM and RH to train our models. First, we predicted the near-surface TEM and RH via mobile monitoring of the TEM, RH and other built environment data. Second, we used near-surface TEM and RH with other urban big data to predict the PM2.5 and O3 concentrations. The results revealed that the estimation performance of the proposed two-stage machine learning prediction models is high, with R2 values above 0.95. Satellite top of atmosphere reflectance (TOA), land surface reflectance (LSR) and street view data were incorporated into the new framework to obtain higher-spatial-resolution (50 m) air pollution maps. Our results revealed that TEM and RH varied considerably between the near-surface and meteorological stations. Accurate near-ground TEM and RH are important for predicting near-ground PM2.5 and O3 concentrations. Furthermore, TOA and LSR are promising for predicting near-ground PM2.5 concentrations.

Aerosol optical depth retrieval using scaled digital number (DN) values of multi-spectral satellite and a generating adversarial model based on deep learning application

ABSTRACT The current quantitative retrieval of Aerosol Optical Depth (AOD) typically uses Top-of-atmosphere (TOA) reflectance data obtained by the scale and offset terms of radiometric calibration. Errors can be introduced during the conversion of Digital Number (DN) values to TOA reflectance, restraining the accurate retrieval of AOD. Particularly when surface reflectance is high, conversion errors can significantly distort the retrieval of Aerosol Optical Depth (AOD), given the minimal impact of aerosols on the radiation detected by satellite sensors. To counteract this, we introduce an innovative retrieval algorithm that harnesses a Generative Adversarial Network (GANN), a deep learning (DL) model, to accurately derive AOD from the scaled digital number (DN) values of multi-spectral satellite imagery. The DL technology enables the use of scaled DN values for AOD retrieval, bypassing the conversion errors associated with TOA reflectance since it relies on a sample learning approach instead of radiative transfer calculations. To ensure the number and representativeness of training samples, a total of 37,103 samples from different underlying surfaces from 2014 to 2018 were obtained using a space-time matching strategy. Our K-cross validation demonstrates that employing DN values for AOD retrieval promises to yield higher accuracy compared to using TOA reflectance. Independent 2019 data were used to estimate GANN, MCD19A2 and MOD04_3K aerosol products. Our model showed superior performance compared to the other products in terms of a higher correlation (R = 0.8184) with AERONET AOD and obtaining more reliable retrievals (7601). The sensitivity experiment suggests that GANN exhibits limitations in areas characterized by high aerosol loads and heterogeneity. Our study can give a novel insight into AOD retrieval based on multi-spectral satellite and DL models.

Estimating Brazilian Amazon Canopy Height Using Landsat Reflectance Products in a Random Forest Model with Lidar as Reference Data

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height.

A Global Mosaic of Temporally Stable Pixels for Radiometric Calibration of Optical Satellite Sensors Using Landsat 8

Calibrating optical sensors has become a priority to maintain data quality and ensure consistency among sensors from different agencies. Achieving and monitoring radiometric calibration often involves the identification of temporally stable targets on the Earth’s surface. Although some locations across North Africa have traditionally been used as primary targets for calibration purposes, it is crucial to explore alternative options to account for potential changes in these sites over time. This study conducted a global assessment of pixel-level temporal stability using Landsat 8 OLI data, with the primary goal of identifying regions suitable for global radiometric calibration efforts. This work followed a two-stage approach, including the testing and selection of an effective combination of statistical tests to differentiate between temporally stable and unstable pixels and the generation of a worldwide mosaic of temporally stable pixels through a per-pixel statistical analysis employing a combination of Spearman’s rho and Pettitt’s test for assessing long-term trends and detecting change points. Notably, comparing the temporal mean top-of-atmosphere (TOA) reflectance before and after applying the generated temporal filter to a site with documented unstable pixels revealed a substantial reduction in mean variation, up to 6%. In addition, slopes observed in the pre-filter mean TOA reflectance, ranging between −0.002 and −0.005, became zero or near-zero and statistically insignificant after the temporal filter was applied, demonstrating a reduction in total uncertainties by 3 to 4%. These findings evidence the potential of this work, placing it as a potential foundation in the continuous search to identify additional targets for global radiometric calibration efforts.

Combining RadCalNet Sites for Radiometric Cross Calibration of Landsat 9 and Landsat 8 Operational Land Imagers (OLIs)

Combining images from multiple Earth Observing (EO) satellites increases the temporal resolution of the data, overcoming the limitations imposed by low revisit time and cloud coverage. However, this requires an intercalibration process to ensure that there is no radiometric difference in top-of-atmosphere (TOA) observations or to quantify any offset in the respective instruments. In addition, combining vicarious calibration processes to the intercalibration of instruments can provide a useful mechanism to validate and compare data from multiple sensors. The Radiometric Calibration Network (RadCalNet) provides automated surface and top-of-atmosphere reflectance data from multiple participating ground sites that can be used for instrument vicarious calibration. We present a comparative analysis of the Landsat 8 and Landsat 9 Operational Land Imagers (OLI) sensors and validate the data by comparing them to measurements from RadCalNet sites as a quantitative intercalibration approach. RadCalNet serves as a common reference for instrument radiometric calibration, providing SI-traceable TOA reflectance with its associated absolute uncertainties. This paper discusses the method of combining data from multiple sites and calculating the weighted average by comparing the TOA reflectance of the instruments and their associated uncertainties. The presented process provides a SI-traceable intercalibration methodology and quantifies the offset and uncertainty in the Landsat 8 and 9 OLI instruments, demonstrating that the two instruments are in good agreement with each other and the data can be reliably cross-correlated and used by the scientific community.

Simultaneous estimation of five temporally regular land variables at seven spatial resolutions from seven satellite data using a multi-scale and multi-depth convolutional neural network

Various satellite sensors have provided a huge amount of observations of Earth's environment at variable spatial and temporal resolutions. Many global coarse-resolution land products have been generated from single-satellite data, but global temporally regular land products at fine spatial resolutions (say 10-30 m) are scarce because of infrequent observations. An ideal inversion framework can estimate multiple global continuous land variables at different spatial resolutions by combining all sources of satellite data. This paper proposes a new framework that can estimate five land variables simultaneously from the top-of-atmosphere (TOA) reflectance acquired by seven satellite sensors based on a multi-scale and multi-depth convolutional neural network (MSDCNN). This framework enables us to estimate temporally regular land variables at as fine as 10 m spatial resolution by transforming information from satellite data at coarser spatial resolutions. The estimated land variables include Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), shortwave albedo, visible albedo, and spectral reflectance. Seven sensors that acquire data at different spatial resolutions, include Visible Infrared Imaging Radiometer Suite (VIIRS) (750 m), Moderate Resolution Imaging Spectroradiometer (MODIS) (500 m), Fengyun-3 (FY-3) Medium Resolution Spectral Imager (MERSI) (250 m), China-Brazil Earth Resources Satellite 04 (CBERS-04) Wide Field Imager (WFI) (73 m), Landsat 8 Operational Land Imager (OLI) (30 m), Gaofen-1 (GF-1) Wide-Field-of-View (16 m) and Sentinel 2 A/B Multispectral Imager (MSI) (10 m). This framework is mainly composed of four steps. First, a Shuffled Complex Evolution (SCE) optimization method is adopted to estimate these variables from VIIRS TOA reference. Second, a joint-output random forest regression (RF) method is used to link the satellite observations and the estimated values from step 1. Third, the six-type multi-resolution satellite observations are used to downscale the VIIRS TOA reflectance to six different spatial resolutions by using the MSDCNN. Finally, the downscaled six-resolution VIIRS TOA reflectance is fed into the multiple-variable RF model to estimate land variables. The framework was validated, and the results based on high-resolution reference maps from ImagineS network and time series shortwave albedo field values from Surface Radiation (SURFRAD) and Integrated Carbon Observation System (ICOS) network show that the retrieved variables had high validation accuracy, with root mean square error (RMSE) ranges of 0.361–0.489 (LAI), 0.023–0.120 (FAPAR), 0.013–0.026 (snow-free shortwave albedo), respectively. Comparison results of the retrieved multi-scale variables with the Sentinel 2 A/B (10 m), Landsat 8 (30 m), Global LAnd Surface Satellite (GLASS, 250 m, 500 m), MODIS (500 m), and VIIRS (750 m) products show that their values were close, with RMSE ranges of 0.107–0.273 (LAI), 0.015–0.027 (FAPAR), 0.003–0.007 (shortwave albedo), 0.001–0.007 (visible albedo), and 0.003–0.025 (spectral reflectance), respectively. The results of the direct validation as well as the product intercomparison show that this novel framework has the potential to be used in estimating global land variables at various spatial scales using a variety of satellite data sources.

Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model.

Implementing a Dual-Spectrometer Approach for Improved Surface Reflectance Estimation

Surface reflectance measurement is an integral part of the vicarious calibration of satellite sensors and the validation of satellite-derived top-of-atmosphere (TOA) and surface reflectance products. A well-known practice for estimating surface reflectance is to conduct a field campaign with a spectrometer and a calibration panel, which is labor-intensive and expensive. To address this issue, the Radiometric Calibration Network, RadCalNet, has been developed, which automatically collects surface reflectance over several selected sites. Neither of these approaches can continuously track the atmosphere, which limits their ability to compensate for atmospheric transmittance change during target measurement. This paper presents the dual-spectrometer approach that uses a stationary spectrometer dedicated to continuously tracking changes in atmospheric transmittance by staring at a calibrated reference panel while the mobile spectrometer measures the target. Simultaneous measurement of the reflectance panel and target help to transfer calibration from the stationary spectrometer to the mobile spectrometer and synchronize the measurements. In this manner, atmospheric transmittance changes during target measurement can be tracked and used to reduce the variability of the target surface reflectance. This paper uses field measurement data from combined field campaigns between different calibration groups at Brookings, South Dakota, and Landsat 8 and Landsat 9 underfly efforts over Coconino National Forest, Arizona, and Guymon, Oklahoma. Preliminary results show that even in a clear sky condition, where atmospheric transmittance changes are minimal, the precision of target surface reflectance estimated using the dual-spectrometer approach is 2–6% better than the single-spectrometer approach. The dual-spectrometer approach shows the potential for a substantial improvement in the precision of the target spectral profile when the atmospheric transmittance is changing rapidly during field measurement. Results show that during non-optimal atmospheric conditions, the dual-spectrometer approach improved the precision of the surface reflectance by 50–60% compared to the single-spectrometer approach across most spectral regions. The ability to estimate surface reflectance more precisely using the dual-spectrometer approach in different atmospheric conditions improves the vicarious calibration of optical satellite sensors and the validation of both TOA and surface reflectance products.

Parameterization of optical properties for liquid cloud droplets containing black carbon based on neural network.

This paper introduces a novel back propagation (BP) neural network method to accurately characterize optical properties of liquid cloud droplets, including black carbon. The model establishes relationships between black carbon volume fraction, wavelength, cloud effective radius, and optical properties. Evaluated on a test set, the value of the root mean square error (RMSE) of the asymmetry factor, extinction coefficient, single-scattering albedo, and the first 4 moments of the Legendre expansion of the phase function are less than 0.003, with the maximum mean relative error (MRE) reaching 0.2%, which are all better than the traditional method that only uses polynomials to fit the relationship between the effective radius and optical properties. Notably, the BP neural network significantly compresses the optical property database size by 37,800 times. Radiative transfer simulations indicate that mixing black carbon particles in water clouds reduces the top-of-atmosphere (TOA) reflectance and heats the atmosphere. However, if the volume fraction of black carbon is less than 10-6, the black carbon mixed in the water cloud has a tiny effect on the simulated TOA reflectance.

PM2.5 Estimation in Day/Night-Time from Himawari-8 Infrared Bands via a Deep Learning Neural Network

Satellite-based PM2.5 estimation is an effective means to achieve large-scale and long-term PM2.5 monitoring and investigation. Currently, most of methods retrieve PM2.5 from satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PM2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PM2.5 concentrations from satellite infrared data, thus achieve the PM2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PM2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PM2.5 concentrations correlates with ground-based data well with R2 of 0.79 and RMSE of 15.43 μg · m−3 for hourly PM2.5 estimation. Spatiotemporal distributions of model-estimated PM2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PM2.5 concentrations during daytime and nighttime in real-time monitoring.

Evaluation of surface reflectance retrieval over diverse surface types using SREM algorithm in varied aerosol conditions for coarse to medium resolution data from multiple spaceborne sensors

ABSTRACT Surface reflectance (SR) is one of the most important components for deriving different biophysical parameters from remotely sensed data. For accurate retrieval of SR, it is essential to accurately estimate the degree of atmospheric alteration to the target signals reaching at-sensor. Simplified and robust SR estimation method (SREM) is one such approximate estimation method that could demonstrate the retrieval of SR from top-of-atmosphere (ToA) signals, without using any precalculated LUTs or aerosol information. The present study attempts to assess the accuracy of the SREM algorithm over varied Indian land surfaces using data from Landsat 8, Sentinel 2, AWiFS and LISS III. The results indicate that while SREM performs quite well for Sentinel 2 over most land surface types, the performance is good for Landsat 8 as well except over water and in coastal aerosol band. SREM retrievals also show high correlation for AWiFS and LISS III but the estimated values are much lower than level 2 SR due to smaller ToA reflectance values. Furthermore, while aerosol load does impact the performance adversely, it is least impactful for Sentinel 2 and LISS III datasets and most over Landsat 8, particularly over water. Additionally, while AWiFS and LISS III SR retrievals are underestimated at all spectral bands, retrieval is slightly underestimated from green to SWIR band in case of Sentinel 2 and SWIR band in case of Landsat 8. Overall, the findings demonstrate that while SREM will need to be modified to adapt to LISS III and AWiFS retrievals, it can, nevertheless, be readily used as a simplified method for quick and approximate estimation of SR over the Indian region using data from Landsat 8 and Sentinel 2.

Validation of EMI-2 Radiometric Performance with TROPOMI over Dome C Site in Antarctica

(1) The Environmental Trace Gases Monitoring Instrument-2(EMI-2) is a high-quality spaceborne imaging spectrometer that launched in September 2021. To evaluate its radiometric calibration performance in-flight, the UV2 and VIS1 bands of EMI-2 were cross-calibrated by the corresponding bands (band3 and band4) of TROPOMI over the pseudo-invariant calibration site Dome C. (2) After angle limitation and cloud filtering of the Earth radiance data measured by EMI-2 and TROPOMI over Dome C, the top of atmosphere (TOA) reflectance time series were calculated. The spectral adjustment factors (SAF) were derived from the solar spectrum measured by the sensor to minimize the uncertainties caused by the different spectral response functions (SRF) of sensors. In addition, a correction method based on the radiative transfer model (RTM) SCIATRAN was used to suppress unaccounted angular dependence of atmospheric scattering. The radiation performance of EMI-2 is evaluated using the TOA reflectance ratio of EMI-2 and TROPOMI, combining the SAF correction and RTM-based correction methods. (3) It was shown that the time series trending of the TOA reflectance ratio between EMI-2 measurements and TROPOMI demonstrate flat characteristics and strong correlation. The mean reflectance ratios range from 0.998 to 1.09. The standard deviation of the reflection ratio is less than 3%. For 328 nm, 335 nm, 340 nm, 460 nm, and 490 nm, the mean values are close to one, and the relative radiometric bias estimated through EMI-2 and TROPOMI intercalibration is less than 3%, and for other wavelengths, the biases are less than 6%, except for 416 nm, which behaves higher than 7%. The cross-calibration results show that the radiometric calibration of EMI-2 is within the relative accuracy requirement.

Topography-adjusted Monte Carlo simulation of the adjacency effect in remote sensing of coastal and inland waters

We present a Monte Carlo radiative transfer (RT) code that simulates the top-of-atmosphere (TOA) reflectance of waterbodies considering the adjacency effect. The code is the first open-source tool that supports modeling of the adjacency effect with arbitrary topography. It uses the same atmospheric and aerosol settings as 6S, along with user-input surface reflective properties and topography, allowing users to transition from 1D to 3D RT modeling and characterize the adjacency effect in their study areas. The calculation of radiometric quantities was validated against libRadtran, showing a maximum difference lower than 0.6% in extreme optical settings. Examples of the use of the code are presented in three case studies where modeled and measured radiative properties align with each other. One case study shows that 83.7% of the variance in the near-infrared TOA reflectance of 47 lakes in Minnesota was explained by the adjacency effect, emphasizing the significance of the adjacency effect to atmospheric correction algorithms that use near-infrared bands to retrieve aerosol and glint information. Another case study supports the finding of strong wavelength dependence of the effective sea-surface reflectance in above-water measurements of remote sensing reflectance. The code will support physics-based methods that remove the adjacency effect in atmospheric correction processes, and it has the potential to improve satellite-based monitoring of coastal and inland waterbodies.

Estimating high-spatial-resolution daily PM2.5 mass concentration from satellite top-of-atmosphere reflectance based on an improved random forest model

The aerosol optical depth (AOD) has been widely used to estimate ground PM2.5 mass concentration in the field of remote sensing, but the current AOD retrieval results always contained invalid AOD values due to the limitations of different algorithms, which is not conducive to obtaining daily estimations of PM2.5 mass concentration. This study used the top-of-atmosphere (TOA) reflectance to establish a relationship with ground PM2.5 mass concentration instead of using traditional AOD product data. We developed an improved multiple-factor random forest model (IMFRFM) by integrating TOA reflectance, meteorological data, and land-use data to estimate high-spatial-resolution (1-km × 1-km) daily PM2.5 mass concentration in the Pearl River Delta (PRD) region. The simulation results showed that our model had excellent performance (test-R2 = 0.88, RMSE = 9.20 μg/m³, and MAE = 2.48 μg/m³) with city-based and time-based cross-validation (CV) test-R2 values of 0.82 and 0.88, respectively. The four-year (2017–2020) seasonal results showed that the PM2.5 mass concentration of the PRD region exhibited obvious seasonality, with the highest values in winter and lowest values in summer. The four-year annual results showed that the PM2.5 mass concentration in the PRD region decreased year by year. The annual average PM2.5 mass concentration in most cities in the PRD region were below 40 μg/m³, indicating that the atmospheric environment of the PRD region was gradually improving.

Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II

To produce real-time ground-level information on particulate matter with a diameter equal to or less than 2.5 μm (PM2.5), many studies have explored the applicability of satellite data, particularly aerosol optical depth (AOD). However, many of the techniques used are computationally demanding; to overcome these challenges, machine learning(ML)-based research has been on the rise. Here, we used ML techniques to directly estimate ground-level PM2.5 concentrations over South Korea using top-of-atmosphere (TOA) reflectance from the Geostationary Ocean Color Imager I (GOCI-I) and its next generation GOCI-II with improved spatial, spectral, and temporal resolutions. Three ML techniques were used to estimate ground-level PM2.5 concentrations: random forest, light gradient boosting machine (LGBM), and artificial neural network. Three schemes were examined based on the input feature composition of the GOCI spectral bands: scheme 1 using all GOCI-I bands, scheme 2 using only GOCI-II bands that overlap with GOCI-I bands, and scheme 3 using all GOCI-II bands. The results showed that LGBM performed better than the other ML models. GOCI–II–based schemes 2 and 3 (determination of coefficient (R2) = 0.85 and 0.85 and root-mean-square-error (RMSE) = 7.69 and 7.82 μg/m3, respectively) performed slightly better than GOCI-I-based scheme 1 (R2 = 0.83 and RMSE = 8.49 μg/m3). In particular, TOA reflectance at a new channel (380 nm) of GOCI-II was identified as the most contributing variable, given its high sensitivity to aerosols. The long-term estimation of PM2.5 concentrations using the proposed models was examined for ground stations located in two major cities. GOCI–II–based models produced a more detailed spatial distribution of PM2.5 concentrations owing to their higher spatial resolution (i.e., 250 m). The use of TOA reflectance data, instead of AOD and other aerosol products commonly used in previous studies, reduced the missing rate of the estimated ground-level PM2.5 concentrations by up to 50%. Our results indicate that the proposed approach using TOA reflectance data from geostationary satellite sensors has great potential for estimating ground-level PM2.5 concentrations for operational purposes.

An assessment of the spatio-temporal dynamics of Landsat-derived aerosol concentration in relation with land cover and road networks in the Lagos megacity.

The interrelationships between air quality, land cover change, and road networks in the Lagos megacity have not been explored. Globally, there are knowledge gaps in understanding these dynamics, especially using remote sensing data. This study used multi-temporal and multi-spectral Landsat imageries at four epochs (2002, 2013, 2015, and 2020) to evaluate the aerosol optical thickness (AOT) levels in relation to land cover and road networks in the Lagos megacity. A look-up table (LUT) was generated using Py6S, a python-based 6S module, to simulate the AOT using land surface reflectance and top of atmosphere reflectance. A comparative assessment of the method against in situ measurements of particulate matter (PM) at different locations shows a strong positive correlation between the imagery-derived AOT values and the PMs. The AOT concentration across the land cover and road networks showed an increasing trend from 2002 to 2020, which could be explained by urbanization in the megacity. The higher concentration of AOT along the major roads is attributed to the high air pollutants released from vehicles, including home/office generators and industries along the road corridors. The continuous rise in pollutant values requires urgent intervention and mitigation efforts. Remote sensing-based AOT monitoring is a possible solution.

高分一号宽幅多光谱影像辐射定标偏差及其植被指数影响

PDF HTML阅读 XML下载 导出引用 引用提醒 高分一号宽幅多光谱影像辐射定标偏差及其植被指数影响 DOI: 10.5846/stxb202205241462 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划课题项目2019YFB2102004）；福建省自然科学基金项目（2021J011190）；厦门理工学院高层次人才项目（4010520004）；江西省水利科技项目（202224ZDKT11） Radiometric calibration bias of the Gaofen-1 WFV multispectral imagery and its influence on vegetation index Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:自2013年4月在轨运行以来，高分一号（GF-1）宽幅（WFV）多光谱相机已实现持续对地观测，为包括生态学在内的相关研究领域与行业应用提供了丰富的数据源。当前场地定标频次偏低以及定标参数更新、发布滞后的现状，一定程度上影响了GF-1 WFV多光谱数据的定量应用。然而，现有文献仅在数据预处理流程中谈及对GF-1 WFV影像的辐射定标处理，很少讨论定标参数选取不当甚至误用产生的可能影响。基于已公布的辐射定标参数（2014-2021年）和4景GF-1 WFV Level1A级影像产品数据，重点围绕辐射定标偏差及其对多光谱波段星上反射率和常用植被指数的影响等展开模型分析和讨论。结果显示：即使两个相邻年份间，在多数情况下误用辐射定标参数会导致不可忽视的波段星上反射率相对偏差；进而给不同类型植被指数的实际应用带来不同程度的挑战。因辐射定标偏差的影响，常用的两波段归一化型植被指数在监测稀疏植被覆盖区时会存在明显的误差；而对高植被覆盖区的监测时采用两波段简单比值型植被指数将面临更大的挑战。针对存档GF-1 WFV Level1A数据的应用需求，提出利用时间距离加权的线性内插法来修正基于公开定标参数的辐射定标结果，并通过案例分析表明了该处理方法的有效性。最后，希望研究结果能引起普通用户对卫星遥感影像辐射定标的关注和重视。 Abstract:Since its on-orbit operation in April 2013, the wide field of view (WFV) sensors onboard Gaofen-1 (GF-1), where "Gaofen" means high resolution in Chinese, have continuously collected multispectral imagery of the Earth's surface. The GF-1 WFV sensors have provided abundant data sources for many fields including ecology. The fact that insufficient site calibration (i.e. once a year) as well as the updates lag of calibration parameters might limit the quantitative application of the GF-1 WFV imagery to a certain extent. However, current literature has only mentioned radiometric calibration of the GF-1 WFV images as a pre-processing procedure, whereas has rarely discussed the possible impacts caused by improper selection or misuse of calibration parameters. Based on the radiometric calibration parameters published for the GF-1 WFV imagery (from 2014 to 2021) and four scenes of the Level1A data products, the issue on radiometric calibration bias was investigated. Furthermore, impacts of the radiometric calibration bias on the top of atmosphere (TOA) reflectance and on several vegetation indices commonly used were discussed accordingly. It showed that, generally, even the calibration difference between two neighboring years were considered, in most cases, improper selection of the calibration parameters could result in a significantly relative bias in the TOA reflectance. The bias in TOA reflectance further challenged different types of vegetation indices with varying degrees of patterns in practice. In particular, mainly due to the radiometric calibration biases, the obvious errors were more likely in the normalized difference vegetation index based on two bands, for monitoring sparse vegetation coverage area. Actually, the normalized difference vegetation index has been commonly used in assessing vegetation status as well as surface dynamics. At the same time, for monitoring high vegetation coverage area, the simple ratio vegetation index with two bands was possibly confronted with greater challenges. Consequently, to make full use of the GF-1 WFV Level1A products, it is critical to solve the radiometric calibration problems. In this study, a time weighted linear interpolation method was proposed. In the interpolation process for a specific GF-1 WFV imagery, both radiometric parameters obtained closely before and after the imagery acquisition were integrated. A case study suggested the effectiveness of the proposed processing to improve the radiometric calibration of the GF-1 WFV images in Level1A products, as compared against the results obtained merely based on public calibration parameters. Finally, general users should pay much attention to the radiometric calibration of satellite remotely sensed data (e.g., the GF-1 WFV multispectral imagery) for their quantitative applications, as discussed in this investigation. 参考文献 相似文献 引证文献

Validation of the TOA Products of the Baotou Sandy Site With Landsat8/OLI Considering BRDF Correction

The Baotou sandy site in China is an important field site in the Committee on Earth Observation Satellites radiometric calibration network (RadCalNet), which freely releases the bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) spectral reflectance products in the spectral range from 400 nm to 1000 nm at 10 nm intervals with the aid of a set of automatic spectral and atmospheric parameter measurement devices. However, the released TOA and BOA spectral reflectance products are nadir-view spectral reflectance, which could produce errors if the Baotou sandy site is offset from the nadir point when they are utilized to calibrate one satellite sensor with the vicarious calibration method or to detect radiometric performance changes in the target sensor. Therefore, to address this problem and further broaden the applicable scopes of TOA spectral reflectance products, this article develops TOA and BOA bidirectional reflectance distribution function (BRDF) models using valid time-series TOA and BOA spectral reflectance products to fulfil BRDF correction. Then, the corrected TOA spectral reflectance is validated with the on-orbit measured TOA spectral reflectance of the Landsat8 Operational Land Imager (OLI) sensor with high and stable radiometric performance. After analysing the fitting accuracies of the BOA and TOA BRDF models, the influences of the filter criteria of valid time-series nadir-view BOA and TOA spectral reflectance, geo-positioning error of Landsat8/OLI, and aerosol type in the BOA BRDF correction process on the validation results are discussed. The results show that the BOA BRDF model obtains a better correction effect and is more stable than the TOA BRDF model.

Aerosol Layer Height Retrieval Over Ocean From the Advanced Himawari Imager Using Spectral Reflectance Sensitivity

Aerosol layer height (ALH) has been retrieved using multi-angle observations or the O2–O2 and O2–A/B absorption bands. This study attempted to retrieve ALH using the Advanced Himawari Imager (AHI), a single passive imager onboard Himawari-8 and -9. ALH retrieval using geostationary Earth orbit (GEO) satellites is advantageous for monitoring diurnal changes in ALH and understanding long-range transport. Before retrieving the ALH, the aerosol optical properties (AOPs) are retrieved using the green-near infrared (NIR) band, which is relatively insensitive to aerosol height. The retrieved AOPs are used as input to the radiative transfer calculation to compute the top-of-atmosphere (TOA) reflectance of a highly sensitive band (the blue band in this study). Then, the ALH is retrieved using the observed and calculated TOA reflectances. Since the retrieval accuracy of the aerosol optical depth (AOD) is better over the ocean, the retrieval was performed only over the ocean during the Korea–United States Air Quality Study (KORUS-AQ) campaign period. The retrieved ALH was validated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and high-spectral-resolution Lidar (HSRL).

The retrieval of aerosol optical properties based on a random forest machine learning approach: Exploration of geostationary satellite images

Aerosol optical properties are among the most fundamental parameters in atmospheric environmental studies. Satellite aerosols retrievals that are based on deep learning or machine learning approach have been widely discussed in remote sensing studies, but the flexible random forest (RF) model has not received much attention in the retrieval of geostationary satellite, like Himawari-8. Thus, the Himawari-8 aerosol retrieval achieved by RF model requires further investigation and optimization. Based on the radiative transfer equation, this study proposed a RF model driven by a differential operator, which quantifies a simple linear relationship between aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance enhancement. The spectral information of aerosols is achieved by independent TOA reflectance comparison between images rather than one result from multiple band synthesis. The method allows simple feature inputs and shows weak dependence on auxiliary data. It also achieves simultaneous retrievals over different surfaces and maintains mathematical correlation between spectral AODs and Angstrom Exponents (AE). The model performance was evaluated using a series of comprehensive temporal and spatial validation analyses. A sample-based tenfold cross-validation (10-CV) shows that the new method can simultaneously improve the estimation of aerosol properties, with considerably high correlation coefficients (R2) of 0.85 for AODs at the 0.50 μm wavelengths, a mean absolute error (MAE) of 0.08, a root mean square error (RMSE) of 0.13 and >70% of the samples fell within the AOD expected error (EE). The high accuracy of the spectral AOD retrievals also exhibits good performance on AE calculations, with at least 2/3 of the samples falling within the EE. The site based 10-CV also evaluates the spatial predictions on AODs at the 0.50 μm wavelength, with R2 of 0.67, MAE of 0.12 and RMSE of 0.18. It also has outperformed the Himawari operational aerosol products and appeared to be comparable to other popular machine learning models with better AE retrievals in some typical regions. Two typical regional pollution cases also highlight the advantages of the new aerosol monitoring approach. The 5 km resolution aerosol retrievals exhibit good spatial coverage and performance when describing the regional pollution levels and types. The proposed method improves the performance of RF in retrieving aerosol properties from geostationary satellites and also offers a new prospective for aerosol remote sensing using machine learning approaches.